Recently, developments in computer and network technologies provide rich resource for retrieving information. Also, the amount of information which can be retrieved from network is quickly increasing at an explosive rate. An effective search means is needed to retrieve the exact information desired. Typically search engines facilitate retrieving information for a user. However, with the continual increase in the amount of the information on network, the corresponding amount of searched results returned which does not meet requirement is increasing, i.e. the irrelevant information returned from a search.
In total class of information search, the people search occupies significant portion. Currently, the people search is normally realized by inputting people names into the image search engines, with the desire that only images in relevant to the people being search will be retrieved. However, current image search engines usually return lots of irrelevant web images by manner of inputting people names to perform search, even images without image of the person at all, reducing users' satisfaction.
Current image search engines search for web images using text-based features, such as image file name, anchor text linking to associated page, or surrounding text. In the case of people search, if a people name is present in the surrounding text, the corresponding images will be returned to the user. However, due to inconsistence between image contents and text descriptions, the user might be presented with images with various contents when searching based on features of text only.
Named Entity Recognition (NER) technology has great development recently which is an important component of an Information Extraction (IE) system, and its main task is to recognize specific names including people names, place names, institution names and time expressions and digital expressions in a text. Current Named Entity Recognition method is mainly divided into rule-based method and statistics-based method.
Generally, the rule-based method is to create some rules for entity recognition by some experienced experts with respect to specific languages, areas, text formats and the like. For example, a people name may be a subject or an object of a certain sentence, dates present in the art may basically follow some format, and so on. Accordingly, entity matching is performed in the text and type of each entity is determined based on these rules.
Another technical idea is the statistics-based method. At first, entities present in a text are manually marked; then, according to the context of each entity, some features are automatically extracted, wherein such features are such as people names are normally followed by verbs, adjectives are normally followed by specified nouns, and so on. At last, the type of each entity is decided by a classification algorithm. Marking language materials does not need wide acknowledge on computing language and can be finished within a short period. Such system may be not or seldom modified when being transplanted to a new area as long as it is trained by new language materials once. In addition, transplanting the statistic-based system to texts in other natural languages is relatively easier.
In research of information extraction, Named Entity Recognition is the most valuable technology currently. According to an evaluation result, the F-exponential (the weighted geometrical average of recall and precision, wherein the weight is set to 1) of Named Entity Recognition task can be up to more than 90%. Existing systems can realize English Named Entity Recognition with high precision, such as “GATE” system (an open-source software), which realizes rule-base and statistics-based methods, respectively, and can achieve a higher precision by combining the two methods. In addition, some commercial systems are focused on Chinese Named Entity Recognition, such as mass Chinese processing system developed by Mass Technology development limited company.
Also, face detection technology has great development recently, and this technology is a typical pattern recognition problem with respect to specific content and is concerned by academics and industry. The task of face detection is to judge whether there is a face in an input image, and if there is a face, position and size of the face are labeled. In general, current technologies can be divided into organ-based method and template-based method.
The organ-based method treats a face as a combination of typical features and/or organs, firstly extracts some important features such as eyes, noses, lips and so on, and then detects the face according to positions of the features and geometrical relationships there between. The template-based method treats the face as a whole pattern, i.e. a two-dimensional pixel matrix. From viewpoint of statistics, a face pattern space is established by a lot of samples of face images, and it is judged whether there is a face based on degree of likeness. Under the two frames, many methods are developed. In addition, face detection using color information is also an effective method.
After determining a face model, a face detection system further needs functions of feature extraction and classification decision. The two functions are both performed relating to the face model. In general, the feature extraction can be implemented in the space domain or frequency domain of the image. The classification decision is an important content which the statistic pattern recognition researches. During the researching, people realize that combining various technologies and using various information can improve the efficiency of face detection methods. This trend will continue into the future.
In related research of the pattern recognition, the face detection is developed maturely and has achieved a certain technical level either in detection speed or in detection precision. There are some systems or products for commercial applications, including Identix®, Viisage® and Cognitec®, all of which can realize the function of detecting the face exactly and quickly in complex environment and background. In addition, the researchers in Institute of Computing Technology and Institute of Automation of Chinese Academy of Science have also developed face detection systems with high precision.
U.S. patent application publication No. US2006/0155684A1 proposes an improved solution for the existing image search methods, which firs performs searching using the existing image search engines according to a query submitted by a user and returns the results, then analyzes the important regions in the images, generates an object concerned by user and extracts the concerned object. Then the images are presented to the user using Multi-Dimension Scaling (MDS) technology according to the likeness between images. The solution analyzes only the important regions in the images. Thus on one hand, determination of the important regions may render the system overburdened, and on the other hand, the actually important information in the images may be omitted. Also, since the solution uses only the existing image search engines to perform image searching, without text analyzing technology to perform people name searching in the texts surrounding the images, the search method although simple, presents a problem with accuracy of relevant search terms.
U.S. patent application publication No. US2006/0253491A1 proposes another improved solution for the existing image search method, which focuses on searching in an image library, and establishes indexes of the image library using recognizable information in the images. The solution firstly analyzes the recognition information such as faces, clothes, decorations and so on of the people in the images, analyzes the texts occurred in the images, associates these information with the images, establishes indexes based on these information, and distinguishes people with different identities according to the recognition information. The patent performs search with respect to the image library but not directly with respect to larger range of images in networks. So if the solution is applied to the large-scale search for the images in networks, the following problem will occur wherein since the patent establishes the image indexes by directly analyzing all the image contents and using Optical Character Recognition (OCR) technology to analyze the texts in the image, without establishing more efficient indexes with respect to particular types of queries to intentionally search a part of images, the load of search processing is extremely large, and since the solution requires complex recognition technologies such as face recognition, the processing is complicated, and the stability and reliability is not adequate.
According what is needed is a method and system to over come the problems encountered in the prior art and to provide an image search method and a system capable of implementing a face search with simplified scale and high efficiency.